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Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Paperback – 31 July 2020

4.8 out of 5 stars 271 ratings

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No Experience? No Problem!

If you don’t have any experience coding, that’s OK too! The first three chapters have been explicitly written in a way that will allow executives, product managers, etc. to understand the most important things they’ll need to know about deep learning. When you see bits of code in the text, try to look them over to get an intuitive sense of what they’re doing. We’ll explain them line by line. The details of the syntax are not nearly as important as a high-level understanding of what’s going on.

What You Need to Know

As we said before, the only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course. It doesn’t matter if you remember little of it right now; we will brush up on it as needed. Khan Academy has great free resources online that can help.

We are not saying that deep learning doesn’t use math beyond high school level, but we will teach you (or direct you to resources that will teach you) the basics you need as we cover the subjects that require them.

The book starts with the big picture and progressively digs beneath the surface, so you may need, from time to time, to put it aside and go learn some additional topic (a way of coding something or a bit of math). That is completely OK, and it’s the way we intend the book to be read. Start browsing it, and consult additional resources only as needed.

Please note that Kindle or other ereader users may need to double-click images to view the full-sized versions.

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About the Author

Sylvain is a former teacher and a Research Scientist at, with a focus on making deep learning more accessible by designing and improving techniques that allow models to train fast on limited resources. Prior to this, Sylvain wrote several books covering the entire curriculum he was teaching in France (published at ditions Dunod) until 2015 in CPGE. CPGE are a French specific two-year program whereby handpicked students who graduated high school follow an intense preparation before sitting for the competitive exam to enter the top engineering and business schools of the country. Sylvain taught computer science and mathematics in that program for seven years. Sylvain is an alumni from cole Normale Sup rieure (Paris, France) where he studied mathematics and has a Master's Degree in mathematics from University Paris XI (Orsay, France). Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a faculty member at Singularity University, and a Young Global Leader with the World Economic Forum. Jeremy's most recent startup, Enlitic, was the first company to apply deep learning to medicine, and has been selected one of the world's top 50 smartest companies by MIT Tech Review two years running. He was previously the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group-purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects. He has many television and other video appearances, including as a regular guest on Australia's highest-rated breakfast news program, a popular talk on, and data science and web development tutorials and discussions.
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Product details

  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 350 pages
  • ISBN-10 ‏ : ‎ 1492045527
  • ISBN-13 ‏ : ‎ 978-1492045526
  • Customer reviews:
    4.8 out of 5 stars 271 ratings

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5.0 out of 5 stars Fastest route to master enough Deep learning to be productive.
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